nanogpt

Training a GPT model from scratch involves building a minimal transformer with causal self-attention and autoregressive generation in PyTorch. You prepare binary tokenized datasets, configure optimization with cosine learning-rate warmup, and execute mixed-precision training to learn language modeling.

Yes, you can reproduce GPT-2 style training on OpenWebText using multiple GPUs. The workflow supports distributed data parallel execution, allowing you to scale training across GPUs while applying gradient accumulation and mixed-precision optimization.

The best way to fine-tune pretrained GPT checkpoints is to adapt the reusable training pipeline to your custom text dataset. You prepare your text data into a binary tokenized format, then apply the existing optimization and mixed-precision training steps to update the model weights.

No, you do not need distributed data parallel execution to train a character-level Shakespeare model. The minimal GPT implementation is compact enough to run character-level training experiments on a CPU or a single GPU without distributed setups.

Mixed-precision training and gradient accumulation optimize memory and throughput in a minimal transformer implementation. Mixed precision uses lower-precision tensors during forward passes, while gradient accumulation simulates larger batch sizes by accumulating gradients over multiple steps before updating weights.

A minimal GPT implementation provides a hackable architecture with tied embeddings and residual blocks to help you understand transformer language modeling. It avoids the opaque abstractions of large frameworks, making it ideal for learning the mechanics of causal self-attention and end-to-end training workflows.